TY - JOUR
T1 - GCSTI
T2 - A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression
AU - Tu, Wenhui
AU - Ling, Guang
AU - Liu, Feng
AU - Hu, Fuyan
AU - Song, Xiangxiang
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's 'internal clock'. To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.
AB - The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's 'internal clock'. To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.
KW - Graph compression
KW - pseudotemporal trajectory inference
KW - pseudotime definition
KW - single-cell data
UR - http://www.scopus.com/inward/record.url?scp=85153372504&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85153372504&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2023.3266109
DO - 10.1109/TCBB.2023.3266109
M3 - Article
C2 - 37037234
AN - SCOPUS:85153372504
SN - 1545-5963
VL - 20
SP - 2945
EP - 2958
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 5
ER -